{"id":"W4321371507","doi":"10.3390/make5010016","title":"A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Corrosion; Pipeline transport; Artificial neural network; Downtime; Reliability (semiconductor); Cathodic protection; Submarine pipeline; Environmental science; Fossil fuel; Pipeline (software); Petroleum engineering; Engineering; Reliability engineering; Computer science; Materials science; Metallurgy; Geotechnical engineering; Environmental engineering; Waste management; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009446142,0.0001719666,0.0001991078,0.0001542676,0.0002702034,0.0000972135,0.00006380681,0.0001282531,0.00008851329],"category_scores_gemma":[0.0004229216,0.0001601629,0.0000369859,0.0002975906,0.00004811432,0.0001944193,0.00004475452,0.0006531478,0.00001299565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004880133,"about_ca_system_score_gemma":0.00001546393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001181497,"about_ca_topic_score_gemma":0.0004208655,"domain_scores_codex":[0.9989368,0.0001400022,0.0002179222,0.0003153982,0.0001987811,0.0001911445],"domain_scores_gemma":[0.9992902,0.0003271355,0.00004851457,0.0001945763,0.00007022788,0.00006936739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004804721,0.00002008019,0.00382257,0.0001041893,0.00004820774,0.00000537093,0.0001563259,0.9438072,0.003132445,0.00002401914,0.000973411,0.04785807],"study_design_scores_gemma":[0.0006572514,0.00003270837,0.02421574,0.0000653246,0.00009027291,0.00001793635,0.00004826074,0.9693072,0.00004340018,0.0001656006,0.005186036,0.0001702612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9519636,0.003767054,0.03358883,0.001067713,0.002115522,0.0006160141,0.00006925833,0.001779111,0.00503292],"genre_scores_gemma":[0.9984108,0.0001120008,0.0002234955,0.00002597634,0.0003213746,0.00001035111,0.0006320477,0.00002210274,0.0002418499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04768781,"threshold_uncertainty_score":0.6531253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05312106828459529,"score_gpt":0.3216352058030846,"score_spread":0.2685141375184893,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}